Source identification of bulk wet deposition in Finland by positive matrix factorization

Anttila, Pia; Paatero, Pentti; Tapper, Unto; Järvinen, Olli

doi:10.1016/1352-2310(94)00367-t

Cited by 174 publications

(94 citation statements)

References 19 publications

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“…Models PMF, a multivariate technique, was used for source apportionment (Paatero and Tapper, 1994 ). Previous PMF applications include identifying sources of bulk wet deposition concentrations of strong acids in Finland ( Antilla et al, 1995) , and more recently, sources of PM from the USEPA Particle TEAM study (Yakovleva et al, 1999 ) and PM in Phoenix urban aerosol ( Ramadan et al, 2000) . PMF incorporates error estimates of the data to solve matrix factorization of a linear model as a constrained, weighted least -squares problem.…”

Section: Datamentioning

confidence: 99%

Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization

Anderson

Miller

Milford

2001

J Expo Sci Environ Epidemiol

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Data from the Total Exposure Assessment Methodology studies, conducted from 1980 to 1987 in New Jersey ( NJ ) and California ( CA ) , and the 1990 California Indoor Exposure study were analyzed using positive matrix factorization, a receptor -oriented source apportionment model. Personal exposure and outdoor concentrations of 14 and 17 toxic volatile organic compounds ( VOCs ) were studied from the NJ and CA data, respectively. Analyzing both the personal exposure and outdoor concentrations made it possible to compare toxic VOCs in outdoor air and exposure resulting from personal activities. Regression analyses of the measured concentrations versus the factor scores were performed to determine the relative contribution of each factor to total exposure concentrations. Activity patterns of the NJ and CA participants were examined to determine whether reported exposures to specific sources correspond to higher estimated contributions from the factor identified with that source. For a subset of VOCs, a preliminary analysis to determine irritancybased contributions of factors to exposures was carried out. Major source types of toxic VOCs in both NJ and CA appear to be aromatic sources resembling automobile exhaust, gasoline vapor, or environmental tobacco smoke for personal exposures, and automobile exhaust or gasoline vapors for outdoor concentrations.

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Section: Datamentioning

confidence: 99%

Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization

Anderson

Miller

Milford

2001

J Expo Sci Environ Epidemiol

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“…Based on Stepanauskas et al (2002), the river loads of particulate organic carbon (POC) were assumed to be equal to the PON load multiplied by the C:N ratio for particulate organic matter in river water. The atmospheric deposition of DOC has been assumed to be constant throughout the study period and based on Anttila et al (1995) amounts to 1 g C m -2 year -1 .…”

Section: Simulation Setupmentioning

confidence: 99%

Key processes in the coupled carbon, nitrogen, and phosphorus cycling of the Baltic Sea

et al. 2017

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In this study we examine pools of carbon (C), nitrogen (N), and phosphorus (P) in the Baltic Sea, both simulated and reconstructed from observations. We further quantify key fluxes in the C, N, and P cycling. Our calculations include pelagic reservoirs as well as the storage in the active sediment layer, which allows a complete coverage of the overall C, N, and P cycling on a system-scale. A striking property of C versus N and P cycling is that while the external supplies of total N and P (TN and TP) are largely balanced by internal removal processes, the total carbon (TC) supply is mainly compensated by a net export out of the system. In other words, external inputs of TN and TP are, in contrast to TC, rather efficiently filtered within the Baltic Sea. Further, there is a net export of TN and TP out of the system, but a net import of dissolved inorganic N and P (DIN and DIP). There is on the contrary a net export of both the organic and inorganic fractions of TC. While the pelagic pools of TC and TP are dominated by inorganic compounds, TN largely consists of organic N because allochthonous organic N is poorly degradable. There are however large basin-wise differences in C, N, and P elemental ratios as well as in inorganic versus organic fractions. These differences reflect both the differing ratios in external loads and differing oxygen conditions determining the redox-dependent fluxes of DIN and DIP.

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“…These features make physical sense in receptor modeling of environmental data. PMF has been successfully applied to the analysis of PTEAM (Particle Total Exposure Assessment Methodology) data from Riverside, CA, 9 and for the source identification of bulk wet deposition in Finland, 10 of particles in Alaska, 11 and of the Arctic aerosol at Alert, N.W.T., Canada. 12 Paterson et al 13 applied PMF to air quality and temperature data collected at a site near the southern end of Lake Michigan in 1997 and used three factors to reproduce 75% of the variation in the data.…”

Section: Introductionmentioning

confidence: 99%

Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

Ramadan

Song

Hopke

2000

Journal of the Air & Waste Management Association

226

147

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Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavyduty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO 4 -2 and OC that may represent coal-fired power plant emissions.For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous IMPLICATIONS A new form of factor analysis, positive matrix factorization, has been applied to PM 10 and two separate sets of PM 2.5 aerosol composition data derived from samples collected in Phoenix, AZ. The results for the two sets of samples were quite comparable, and the results overall show a good resolution of the major sources of atmospheric PM.

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Source identification of bulk wet deposition in Finland by positive matrix factorization

Cited by 174 publications

References 19 publications

Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization

Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization

Key processes in the coupled carbon, nitrogen, and phosphorus cycling of the Baltic Sea

Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

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